linear least-squares for large-scale dense matrix

I use the function "cv::solve(src1, src2, dst, DECOMP_SVD)" to solve a least-squares problem (i.e., src1 * dst = src2). The size of src1 is 29030 * 7809 and the size of src2 is 29030 * 122. Both types of src1 and src2 are CV_32FC1. OpenCV crashes at the 1370 line of "lapack.cpp" and the code of this line is "buffer.allocate(bufsize)". "buffer" is a "AutoBuffer< uchar >" and "bufsize" is 1151016404. Is this problem caused by out-of-memory? My physical memory is 8G and operating system is 64bit Win7.

Comments

1

My c++ knowledge is not the best, you know. But I think your Matrices are getting allocated on the stack which is kind of small, but fast. You should maybe create them on the heap. So try it like this: Mat *A = new Mat(29030, 7809, CV_32FC1); Mat *B = new Mat(29030, 122, CV_32FC1);

I could of course be totally wrong, but at the moment you are trying to allocate 878 MByte on the stack :P

SVD method has very high numerical robustness, but this robustness has its price both in space and in time. If you don't expect a degenerate case you can you much lighter methods that should be able to run on x86. For example: